A Statistical Characterization of Shadowed Fading in Indoor Off-Body Communications Channels At 5.8 GHz

This paper investigates the characteristics of the shadowed fading observed in off-body communications channels at 5.8 GHz using the κ-μ / gamma composite fading model. Realistic measurements have been conducted considering four individual scenarios namely line of sight (LOS) and non-LOS (NLOS) walking, rotation and random movements within an indoor laboratory environment. It is shown that the κ-μ / gamma composite fading model provides a better fit to the fading observed in off-body communications channels compared to the conventional Nakagami-m and Rician fading models.

Validation of night blindness reports among children and women in a vitamin A deficient population in rural Tanzania.

OBJECTIVE: This study validates different definitions of reported night blindness (XN) in a vitamin A deficient African population with no local term for XN. DESIGN: Case-control study with follow-up after treatment. SETTING: Eight primary schools and health centres in rural Tanzania. SUBJECTS: A total of 1214 participants were screened for reported XN and other eye signs of xerophthalmia: 461 children aged 24-71 months, 562 primary school-age children and 191 pregnant or breast-feeding women. All 152 cases of reported XN were selected for the validation study and group matched with 321 controls who did not complain of XN. XN reports were validated against serum retinol concentrations and pupillary dark adaptation measurements in cases and controls. INTERVENTION: All children and women who reported XN or had other signs of active xerophthalmia were treated with vitamin A and followed up 3-4 weeks later. Half of the untreated control group who had their serum retinol examined in the baseline examination were also followed up. RESULTS: The overall prevalence of reported XN was 12.5%. At baseline, mean pupillary threshold (-1.52 vs -1.55 log cd/m(2), P=0.501) and median serum retinol concentrations (0.95 vs 0.93 micromol/l, P=0.734) were not significantly different in cases and controls either overall or in each population group. More restricted case definitions reduced the prevalence of reported XN to 5.5% (P<0.001), but there was still no significant difference between cases and controls although the results were in the expected direction. After treatment, the median serum retinol concentration improved significantly only in the most deficient group, the young children. Dark adaptation improved in all the subgroups but the difference was only significant for young children and primary school-age children when the restricted case definitions were used. CONCLUSIONS: XN reports are a poor indicator of vitamin A deficiency in this population. SPONSORSHIP: Task Force Sight and Life, Basel, Switzerland.

Reliability of reports of childhood trauma in bipolar disorder: A test–retest study over 18 months

This study aimed to explore the reliability of self-reported trauma histories in a population with a diagnosis of Bipolar Disorder using the Childhood Trauma Questionnaire. Previous studies in other populations suggest high reliability of trauma histories over time and it was postulated that a similar high reliability would be demonstrated in this population. Thirty-nine patients with a confirmed diagnosis (DSM-IV criteria) were followed-up and re-administered the Childhood Trauma Questionnaire after 18 months. Cohen's kappa scores and intraclass correlations suggest reasonable test-retest reliability over the 18-month time period of the study for all types of childhood abuse, namely emotional, physical, sexual, and physical abuse and emotional neglect. Intraclass correlations ranged from r = .50 to (sexual abuse) to r = .96 (physical abuse). Cohen's kappas ranged from .44 (sexual abuse) to .76 (physical abuse). Retrospective reports of childhood trauma can be seen as reliable and are in keeping with results found with other mental health populations.

A high-value, low-cost bubble continuous positive airway pressure system for low-resource settings:technical assessment and initial case reports

Acute respiratory infections are the leading cause of global child mortality. In the developing world, nasal oxygen therapy is often the only treatment option for babies who are suffering from respiratory distress. Without the added pressure of bubble Continuous Positive Airway Pressure (bCPAP) which helps maintain alveoli open, babies struggle to breathe and can suffer serious complications, and frequently death. A stand-alone bCPAP device can cost $6,000, too expensive for most developing world hospitals. Here, we describe the design and technical evaluation of a new, rugged bCPAP system that can be made in small volume for a cost-of-goods of approximately $350. Moreover, because of its simple design--consumer-grade pumps, medical tubing, and regulators--it requires only the simple replacement of a <$1 diaphragm approximately every 2 years for maintenance. The low-cost bCPAP device delivers pressure and flow equivalent to those of a reference bCPAP system used in the developed world. We describe the initial clinical cases of a child with bronchiolitis and a neonate with respiratory distress who were treated successfully with the new bCPAP device.

Shadowed Fading in Indoor Off-Body Communications Channels: A Statistical Characterization using the κ – μ / Gamma Composite Fading Model

This paper investigates the characteristics of the shadowed fading observed in off-body communications channels at 5.8 GHz. This is realized with the aid of the $\kappa-\mu$ / gamma composite fading model which assumes that the transmitted signal undergoes $\kappa-\mu$ fading which is subject to \emph{multiplicative} shadowing. Based on this, the total power of the multipath components, including both the dominant and scattered components, is subject to non-negligible variations that follow the gamma distribution. For this model, we present an integral form of the probability density function (PDF) as well as important analytic expressions for the PDF, cumulative distribution function, moments and moment generating function. In the case of indoor off-body communications, the corresponding measurements were carried out in the context of four explicit individual scenarios namely: line of sight (LOS) and non-LOS (NLOS) walking, rotational and random movements. The measurements were repeated within three different indoor environments and considered three different hypothetical body worn node locations. With the aid of these results, the parameters for the $\kappa-\mu$ / gamma composite fading model were estimated and analyzed extensively. Interestingly, for the majority of the indoor environments and movement scenarios, the parameter estimates suggested that dominant signal components existed even when the direct signal path was obscured by the test subject's body. Additionally, it is shown that the $\kappa-\mu$ / gamma composite fading model provides an adequate fit to the fading effects involved in off-body communications channels. Using the Kullback-Leibler divergence, we have also compared our results with another recently proposed shadowed fading model, namely the $\kappa-\mu$ / lognormal LOS shadowed fading model. It was found that the $\kappa-\mu$ / gamma composite fading model provided a better fit for the majority of the scenarios considered in this study.

Statistical Tests for Joint Analysis of Performance Measures

Recently there has been an increasing interest in the development of new methods using Pareto optimality to deal with multi-objective criteria (for example, accuracy and architectural complexity). Once one has learned a model based on their devised method, the problem is then how to compare it with the state of art. In machine learning, algorithms are typically evaluated by comparing their performance on different data sets by means of statistical tests. Unfortunately, the standard tests used for this purpose are not able to jointly consider performance measures. The aim of this paper is to resolve this issue by developing statistical procedures that are able to account for multiple competing measures at the same time. In particular, we develop two tests: a frequentist procedure based on the generalized likelihood-ratio test and a Bayesian procedure based on a multinomial-Dirichlet conjugate model. We further extend them by discovering conditional independences among measures to reduce the number of parameter of such models, as usually the number of studied cases is very reduced in such comparisons. Real data from a comparison among general purpose classifiers is used to show a practical application of our tests.

A statistical framework for channel availability modelling in the polarisation domain

Cognitive radio has been proposed as a means of improving the spectrum utilisation and increasing spectrum efficiency of wireless systems. This can be achieved by allowing cognitive radio terminals to monitor their spectral environment and opportunistically access the unoccupied frequency channels. Due to the opportunistic nature of cognitive radio, the overall performance of such networks depends on the spectrum occupancy or availability patterns. Appropriate knowledge on channel availability can optimise the sensing performance in terms of spectrum and energy efficiency. This work proposes a statistical framework for the channel availability in the polarization domain. A Gaussian Normal approximation is used to model real-world occupancy data obtained through a measurement campaign in the cellular frequency bands within a realistic scenario.

Victim impact reports in Northern Ireland: Victims’ voices influencing sentencing?

Discusses three Northern Ireland Court of Appeal decisions concerning the role of victim impact reports (VIRs) on sentencing in sexual violence cases, and illustrating how courts may be unable to rely on victims' accounts of the harm they suffered because the experts' reports were unreliable. Details key features of the cases, the use of VIRs as evidence-based harm, and why improved guidance on their use is needed in Northern Ireland.

Medical information extraction in European Portuguese

The electronic storage of medical patient data is becoming a daily experience in most of the practices and hospitals worldwide. However, much of the data available is in free-form text, a convenient way of expressing concepts and events, but especially challenging if one wants to perform automatic searches, summarization or statistical analysis. Information Extraction can relieve some of these problems by offering a semantically informed interpretation and abstraction of the texts. MedInX, the Medical Information eXtraction system presented in this document, is the first information extraction system developed to process textual clinical discharge records written in Portuguese. The main goal of the system is to improve access to the information locked up in unstructured text, and, consequently, the efficiency of the health care process, by allowing faster and reliable access to quality information on health, for both patient and health professionals. MedInX components are based on Natural Language Processing principles, and provide several mechanisms to read, process and utilize external resources, such as terminologies and ontologies, in the process of automatic mapping of free text reports onto a structured representation. However, the flexible and scalable architecture of the system, also allowed its application to the task of Named Entity Recognition on a shared evaluation contest focused on Portuguese general domain free-form texts. The evaluation of the system on a set of authentic hospital discharge letters indicates that the system performs with 95% F-measure, on the task of entity recognition, and 95% precision on the task of relation extraction. Example applications, demonstrating the use of MedInX capabilities in real applications in the hospital setting, are also presented in this document. These applications were designed to answer common clinical problems related with the automatic coding of diagnoses and other health-related conditions described in the documents, according to the international classification systems ICD-9-CM and ICF. The automatic review of the content and completeness of the documents is an example of another developed application, denominated MedInX Clinical Audit system.

Pregnancy and newborns disorders followed by urine metabolomics

Chapter 1 introduces the scope of the work by identifying the clinically relevant prenatal disorders and presently available diagnostic methods. The methodology followed in this work is presented, along with a brief account of the principles of the analytical and statistical tools employed. A thorough description of the state of the art of metabolomics in prenatal research concludes the chapter, highlighting the merit of this novel strategy to identify robust disease biomarkers. The scarce use of maternal and newborn urine in previous reports enlightens the relevance of this work. Chapter 2 presents a description of all the experimental details involved in the work performed, comprising sampling, sample collection and preparation issues, data acquisition protocols and data analysis procedures. The proton Nuclear Magnetic Resonance (NMR) characterization of maternal urine composition in healthy pregnancies is presented in Chapter 3. The urinary metabolic profile characteristic of each pregnancy trimester was defined and a 21-metabolite signature found descriptive of the metabolic adaptations occurring throughout pregnancy. 8 metabolites were found, for the first time to our knowledge, to vary in connection to pregnancy, while known metabolic effects were confirmed. This chapter includes a study of the effects of non-fasting (used in this work) as a possible confounder. Chapter 4 describes the metabolomic study of 2nd trimester maternal urine for the diagnosis of fetal disorders and prediction of later-developing complications. This was achieved by applying a novel variable selection method developed in the context of this work. It was found that fetal malformations (FM) (and, specifically those of the central nervous system, CNS) and chromosomal disorders (CD) (and, specifically, trisomy 21, T21) are accompanied by changes in energy, amino acids, lipids and nucleotides metabolic pathways, with CD causing a further deregulation in sugars metabolism, urea cycle and/or creatinine biosynthesis. Multivariate analysis models´ validation revealed classification rates (CR) of 84% for FM (87%, CNS) and 85% for CD (94%, T21). For later-diagnosed preterm delivery (PTD), preeclampsia (PE) and intrauterine growth restriction (IUGR), it is found that urinary NMR profiles have early predictive value, with CRs ranging from 84% for PTD (11-20 gestational weeks, g.w., prior to diagnosis), 94% for PE (18-24 g.w. pre-diagnosis) and 94% for IUGR (2-22 g.w. pre-diagnosis). This chapter includes results obtained for an ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) study of pre-PTD samples and correlation with NMR data. One possible marker was detected, although its identification was not possible. Chapter 5 relates to the NMR metabolomic study of gestational diabetes mellitus (GDM), establishing a potentially predictive urinary metabolic profile for GDM, 2-21 g.w. prior to diagnosis (CR 83%). Furthermore, the NMR spectrum was shown to carry information on individual phenotypes, able to predict future insulin treatment requirement (CR 94%). Chapter 6 describes results that demonstrate the impact of delivery mode (CR 88%) and gender (CR 76%) on newborn urinary profile. It was also found that newborn prematurity, respiratory depression, large for gestational age growth and malformations induce relevant metabolic perturbations (CR 82-92%), as well as maternal conditions, namely GDM (CR 82%) and maternal psychiatric disorders (CR 91%). Finally, the main conclusions of this thesis are presented in Chapter 7, highlighting the value of maternal or newborn urine metabolomics for pregnancy monitoring and disease prediction, towards the development of new early and non-invasive diagnostic methods.

Metabolic signature of lung cancer: a metabolomic study of human tissues and biofluids

This thesis reports the application of metabolomics to human tissues and biofluids (blood plasma and urine) to unveil the metabolic signature of primary lung cancer. In Chapter 1, a brief introduction on lung cancer epidemiology and pathogenesis, together with a review of the main metabolic dysregulations known to be associated with cancer, is presented. The metabolomics approach is also described, addressing the analytical and statistical methods employed, as well as the current state of the art on its application to clinical lung cancer studies. Chapter 2 provides the experimental details of this work, in regard to the subjects enrolled, sample collection and analysis, and data processing. In Chapter 3, the metabolic characterization of intact lung tissues (from 56 patients) by proton High Resolution Magic Angle Spinning (HRMAS) Nuclear Magnetic Resonance (NMR) spectroscopy is described. After careful assessment of acquisition conditions and thorough spectral assignment (over 50 metabolites identified), the metabolic profiles of tumour and adjacent control tissues were compared through multivariate analysis. The two tissue classes could be discriminated with 97% accuracy, with 13 metabolites significantly accounting for this discrimination: glucose and acetate (depleted in tumours), together with lactate, alanine, glutamate, GSH, taurine, creatine, phosphocholine, glycerophosphocholine, phosphoethanolamine, uracil nucleotides and peptides (increased in tumours). Some of these variations corroborated typical features of cancer metabolism (e.g., upregulated glycolysis and glutaminolysis), while others suggested less known pathways (e.g., antioxidant protection, protein degradation) to play important roles. Another major and novel finding described in this chapter was the dependence of this metabolic signature on tumour histological subtype. While main alterations in adenocarcinomas (AdC) related to phospholipid and protein metabolisms, squamous cell carcinomas (SqCC) were found to have stronger glycolytic and glutaminolytic profiles, making it possible to build a valid classification model to discriminate these two subtypes. Chapter 4 reports the NMR metabolomic study of blood plasma from over 100 patients and near 100 healthy controls, the multivariate model built having afforded a classification rate of 87%. The two groups were found to differ significantly in the levels of lactate, pyruvate, acetoacetate, LDL+VLDL lipoproteins and glycoproteins (increased in patients), together with glutamine, histidine, valine, methanol, HDL lipoproteins and two unassigned compounds (decreased in patients). Interestingly, these variations were detected from initial disease stages and the magnitude of some of them depended on the histological type, although not allowing AdC vs. SqCC discrimination. Moreover, it is shown in this chapter that age mismatch between control and cancer groups could not be ruled out as a possible confounding factor, and exploratory external validation afforded a classification rate of 85%. The NMR profiling of urine from lung cancer patients and healthy controls is presented in Chapter 5. Compared to plasma, the classification model built with urinary profiles resulted in a superior classification rate (97%). After careful assessment of possible bias from gender, age and smoking habits, a set of 19 metabolites was proposed to be cancer-related (out of which 3 were unknowns and 6 were partially identified as N-acetylated metabolites). As for plasma, these variations were detected regardless of disease stage and showed some dependency on histological subtype, the AdC vs. SqCC model built showing modest predictive power. In addition, preliminary external validation of the urine-based classification model afforded 100% sensitivity and 90% specificity, which are exciting results in terms of potential for future clinical application. Chapter 6 describes the analysis of urine from a subset of patients by a different profiling technique, namely, Ultra-Performance Liquid Chromatography coupled to Mass Spectrometry (UPLC-MS). Although the identification of discriminant metabolites was very limited, multivariate models showed high classification rate and predictive power, thus reinforcing the value of urine in the context of lung cancer diagnosis. Finally, the main conclusions of this thesis are presented in Chapter 7, highlighting the potential of integrated metabolomics of tissues and biofluids to improve current understanding of lung cancer altered metabolism and to reveal new marker profiles with diagnostic value.

A computational study of ionic liquids used in the production of fuels and biofuels

For the past decades it has been a worldwide concern to reduce the emission of harmful gases released during the combustion of fossil fuels. This goal has been addressed through the reduction of sulfur-containing compounds, and the replacement of fossil fuels by biofuels, such as bioethanol, produced in large scale from biomass. For this purpose, a new class of solvents, the Ionic Liquids (ILs), has been applied, aiming at developing new processes and replacing common organic solvents in the current processes. ILs can be composed by a large number of different combinations of cations and anions, which confer unique but desired properties to ILs. The ability of fine-tuning the properties of ILs to meet the requirements of a specific application range by mixing different cations and anions arises as the most relevant aspect for rendering ILs so attractive to researchers. Nonetheless, due to the huge number of possible combinations between the ions it is required the use of cheap predictive approaches for anticipating how they will act in a given situation. Molecular dynamics (MD) simulation is a statistical mechanics computational approach, based on Newton’s equations of motion, which can be used to study macroscopic systems at the atomic level, through the prediction of their properties, and other structural information. In the case of ILs, MD simulations have been extensively applied. The slow dynamics associated to ILs constitutes a challenge for their correct description that requires improvements and developments of existent force fields, as well as larger computational efforts (longer times of simulation). The present document reports studies based on MD simulations devoted to disclose the mechanisms of interaction established by ILs in systems representative of fuel and biofuels streams, and at biomass pre-treatment process. Hence, MD simulations were used to evaluate different systems composed of ILs and thiophene, benzene, water, ethanol and also glucose molecules. For the latter molecules, it was carried out a study aiming to ascertain the performance of a recently proposed force field (GROMOS 56ACARBO) to reproduce the dynamic behavior of such molecules in aqueous solution. The results here reported reveal that the interactions established by ILs are dependent on the individual characteristics of each IL. Generally, the polar character of ILs is deterministic in their propensity to interact with the other molecules. Although it is unquestionable the advantage of using MD simulations, it is necessary to recognize the need for improvements and developments of force fields, not only for a successful description of ILs, but also for other relevant compounds such as the carbohydrates.

The intra-industry effects of going concern withdrawn audit reports

Dissertação de mestrado, Finanças Empresariais, Faculdade de Economia, Universidade do Algarve, 2014